Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method

[1] H. Xu, Y. Jia, Z. Sun, J. Su, Q.S. Liu, Q. Zhou, and G. Jiang, "Environmental pollution, a hidden culprit for health issues, " Eco-Environment & Health, Vol.1, No.1, pp.31-45, 2022.

[2] Z. Zhao, J. Yang, Z. Zhang, S.Wang, Z. Zhang, and J. Lu, "New method for efficient control of hydrogen sulfide and methane in gravity sewers: Combination of NaOH and Nitrite, " Front. Environ. Sci. Eng., Vol. 16, No. 6, pp. 1-14, 2021.

[3] Y. Wang, P. Ning, R. Zhao, K. Li, C. Wang, X. Sun, X. Song, and Q. Lin, "A Cu-modified active carbon fiber significantly promoted H2S and PH3 simultaneous removal at a low reaction temperature, " Front. Environ. Sci. Eng., "Vol. 15, No. 6, pp. 1-10, 2021.

[4] S. Sun, T. Jia, K. Chen, Y. Peng, and L. Zhang, "Simultaneous removal of hydrogen sulfide and volatile organic sulfur compounds in off-gas mixture from a wastewater treatment plant using a two-stage bio-trickling filter system, " Front. Environ. Sci. Eng., Vol. 13, No. 4, pp. 1-13, 2019.

[5] L. Fortuna, A. Rizzo, M. Sinatra, and M.G. Xibilia, "Soft analyzers for a sulfur recovery unit, " Control Engineering Practice, Vol. 11, No. 12, pp. 1491-1500, 2003.

[6] Y. Yu, M. Peng, H. Wang, Z. Ma, and W. Li, "Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant, " Annals of Nuclear Energy, Vol. 148, p. 107662, 2020.

[7] Y.S. Perera, D.A.A.C. Ratnaweera, C.H. Dasanayaka, and C. Abeykoon, "The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review, " Engineering Applications of Artificial Intelligence, Vol. 121, p.105988, 2023.

[8] G.K. Uyanık and N. Güler, "A study on multiple linear regression analysis, " Procedia-Social and Behavioral Sciences, Vol. 106, pp. 234-240, 2013.

[9] Q. Zhu, "Dynamic Autoregressive Partial Least Squares for Supervised Modeling, " IFAC-PapersOnLine, Vol, 54, No. 7, pp. 234-239, 2021.

[10] D. Yan, F.Yang, F.Yang, H. Zhang, Z. Guo, J. Li, and Y. Wu, "Identifying the key system parameters of the organic Rankine cycle using the principal component analysis based on an experimental database, " Energy Conversion and Management, Vol. 240, p. 114252, 2021.

[11] A.J. Burnham, R. Viveros, and J.F. MacGregor, "Frameworks for latent variable multivariate regression, " Journal of chemometrics, Vol. 10, No. 1, pp. 31-45, 1996.

[12] J. Huang, T. Jin, M. Liang, and H. Chen, "Prediction of heat exchanger performance in cryogenic oscillating flow conditions by support vector machine, " Applied Thermal Engineering, Vol. 182, p. 116053, 2021.

[13] S. Kamat and K. Madhavan, "Developing ANN based virtual/soft sensors for industrial problems, " IFACPapersOnLine, Vol. 49, No. 1, pp. 100-105, 2016.

[14] P.V. de Campos Souz, "Fuzzy neural networks and neurofuzzy networks: A review the main techniques and applications used in the literature, " Applied soft computing, Vol. 92, p. 106275, 2020.

[15] J. Chen, J. Yu, and Y. Zhang, "Multivariate video analysis and Gaussian process regression model based soft sensor for online estimation and prediction of nickel pellet size distributions, " Computers & Chemical Engineering, Vol. 64, pp. 13-23, 2014.

[16] E. Zamprogna, M. Barolo, and D.E. Seborg, "Development of a soft sensor for a batch distillation column using linear and nonlinear PLS regression techniques, " IFAC Proceedings Volumes, Vol. 35, No. 1, pp. 431-436, 2002.

[17] Q.X. Zhu, X. Wang, Y.L. He, and Y. Xu, "An improved extreme learning machine integrated with nonlinear principal components and its application to modeling complex chemical processes, " Applied Thermal Engineering, Vol. 130, pp. 745-753, 2018.

[18] X. Zhang, W. Yan, and H. Shao, "Nonlinear multivariate quality estimation and prediction based on kernel partial least squares, " Industrial & engineering chemistry research, Vol. 47, No. 4, pp. 1120-1131, 2008.

[19] S.W. Choi and I.B. Lee, "Nonlinear dynamic process monitoring based on dynamic kernel PCA, " Chemical engineering science, Vol. 59, No. 24, pp. 5897-5908, 2004.

[20] W. Zhou, Y. Cheng, S. Ding, L. Chen, and R. Li, "A grey seasonal least square support vector regression model for time series forecasting, " ISA transactions, Vol. 114, pp. 82- 98, 2021.

[21] P. Kadlec, R. Grbić, and B. Gabrys, "Review of adaptation mechanisms for data-driven soft sensors, " Computers & chemical engineering, Vol. 35, No. 1, pp.1-24, 2011.

[22] Y. Liu, D. Huang, B. Liu, Q. Feng, and B. Cai, "Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries, " Applied Soft Computing, Vol. 101, p. 107060, 2021.

[23] I. Portnoy, K. Melendez, H. Pinzon, and M. Sanjuan, "An improved weighted recursive PCA algorithm for adaptive fault detection, " Control Engineering Practice, Vol. 50, pp.69-83, 2016.

[24] W.S. Yeo, A. Saptoro, P. Kumar, and M. Kano, "Just-in-time based soft sensors for process industries: A status report and recommendations, " Journal of Process Control, Vol. 128, p.103025, 2023.

[25] K.Fujiwara, M. Kano, S. Hasebe, and A. Takinami, "Soft‐ sensor development using correlation‐based just‐in‐ time modeling, " AIChE Journal, No. 55, Vol. 7, pp. 1754-1765, 2009.

[26] K. Liu, W. Shao, and G. Chen, "Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development, " ISA transactions, Vol. 103, pp. 143-155, 2020.

[27] M. Frye, J. Mohren, and RH. Schmitt, "Benchmarking of Data Preprocessing Methods for Machine LearningApplications in Production, " Procedia CIRP, Vol. 104, pp. 50-55, 2021.

[28] S. Cohen, Dealing with data: strategies of preprocessing data. In: Artificial Intelligence and Deep Learning in Pathology, Amsterdam, Netherlands: Elsevier, 2021, pp. 77-92.

[29] Huber P.J., " Robust statistics " volume 523, John Wiley & Sons: Hoboken, New Jersey, United States (2004).

[30] L. Ma, M. Wang, and K. Peng, "A missing manufacturing process data imputation framework for nonlinear dynamic soft sensor modeling and its application," Expert Systems with Applications, Vol. 237, p. 121428, 2024.

[31] B. Bidar, J. Sadeghi, F. Shahraki, and M.M. Khalilipor, "Data-driven soft sensor approach for online quality prediction using state dependent parameter models, " Chemometrics and Intelligent Laboratory Systems, Vol. 162,
pp. 130-141, 2017.

[32] R. Parvizi Moghadam, J. Sadeghi, and F. Shahraki, "Optimization of time ‐ variable ‐ parameter model for data based soft sensor of industrial debutanizer, " Optimal Control Applications and Methods, Vol. 41, No.2, pp. 381- 394, 2020.

[33] B. Bidar, F. Shahraki, J. Sadeghi, and M.M. Khalilipor, "Soft sensor modeling based on multi-state-dependent parameter models and application for quality monitoring in industrial sulfur recovery process, " IEEE Sensors Journal, Vol. 18, No. 11, pp. 4583-4591, 2018.

[34] B. Bidar, M.M. Khalilipor, F. Shahraki, and J. Sadeghi, "A data-driven soft-sensor for monitoring ASTM-D86 of CDU side products using local instrumental variable (LIV) technique, " Journal of the Taiwan Institute of Chemical Engineers, Vol. 84, pp. 49-59, 2018.

[35] DM. Hawkins, S.C. Basak, and D. Mills, "Assessing model fit by cross-validation, " Journal of chemical information and computer sciences, Vol. 43, No. 2, pp.579-586, 2003.

[36] B. Sprague, Q. Shi, M.T. Kim, L. Zhang, A. Sedykh, E. Ichiishi, H. Tokuda, K.H. Lee, and H. Zhu, "Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers, " Journal of computeraided molecular design, Vol. 28, No. 6, pp. 631-646, 2014.

[37] Z. Ge and Z. Song, "Nonlinear soft sensor development based on relevance vector machine, " Industrial & engineering chemistry research, Vol. 49, No. 18, pp. 8685-8693, 2010.

[38] W. Shao, X. Tian, and P. Wang, "Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division, " Chinese Journal of Chemical Engineering, Vol. 22, No. 7, pp. 828-836, 2014.

[39] W. Shao and X. Tian, "Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models, " Chemical Engineering Research and Design, Vol. 95, pp.113-132, 2015.

[40] V. Jain, P. Kishore, R.A. Kumar, and A.K. Pani, "Inferential sensing of output quality in petroleum refinery using principal component regression and support vector regression, " In: 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, India, January 5-7, 2017, Piscataway: IEEE, 2017, pp. 461-465.

[41] W. Shao and X. Tian, "Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development, " Neurocomputing, Vol. 222, pp. 91-104, 2017.

[42] X. Chen, Z. Mao, R. Jia, and S. Zhang, "Ensemble regularized local finite impulse response models and soft sensor application in nonlinear dynamic industrial processes, " Applied Soft Computing, Vol. 85, p. 105806, 2019.

[43] B. Alakent, "Soft sensor design using transductive moving window learner, " Computers & Chemical Engineering, Vol. 140, p.106941, 2020.

[44] A. Urhan and B. Alakent, "Integrating adaptive moving window and just-in-time learning paradigms for soft sensor design, " Neurocomputing, Vol. 392, pp. 23-37, 2020.

[45] B. Alakent, "Soft-sensor design via task transferred just-intime-learning coupled transductive moving window learner, " Journal of Process Control, Vol. 101, pp. 52-67, 2021.

[46] S. Liu, and S. Li, "A semi-supervised soft sensor method based on vine copula regression and tri-training algorithm for complex chemical processes," Journal of Process Control, Vol. 120, pp.115-128, 2022.

[47] V. Vijayan, H.K. Mohanta, and A.K. Pani, " Adaptive nonlinear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach," Applied Soft Computing, Vol. 119, p.108546, 2022.

[48] T. Zhang, G. Yan, M. Ren, L. Cheng, R. Li, and G. Xie, " Dynamic transfer soft sensor for concept drift adaptation," Journal of Process Control, Vol. 123, pp.50-63, 2023.

[49] Y. Zhao, X. Deng, and S. Li, " A nonlinear industrial soft sensor modeling method based on locality preserving stochastic configuration network with utilizing unlabeled samples, " ISA transactions, 2023.